%matplotlib inline

# OPTIONAL: Load the "autoreload" extension so that code can change
%load_ext autoreload

# OPTIONAL: always reload modules so that as you change code in src, it gets loaded
%autoreload 2

Data

from pvlib.iotools import read_epw
weather, meta = read_epw('../data/processed/meteo.epw')
weather.head()

	year	month	day	hour	minute	data_source_unct	temp_air	temp_dew	relative_humidity	atmospheric_pressure	...	ceiling_height	present_weather_observation	present_weather_codes	precipitable_water	aerosol_optical_depth	snow_depth	days_since_last_snowfall	albedo	liquid_precipitation_depth	liquid_precipitation_quantity
2005-01-01 00:00:00+01:00	2005	1	1	1	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	7.1	1.5	68	98414	...	99999	9	999999999	10	0.118	18	0	0.732	0.0	99.0
2005-01-01 01:00:00+01:00	2005	1	1	2	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.7	1.5	69	98414	...	99999	9	999999999	10	0.118	18	0	0.717	0.0	99.0
2005-01-01 02:00:00+01:00	2005	1	1	3	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.4	1.0	68	98414	...	99999	9	999999999	10	0.118	18	0	0.702	0.0	99.0
2005-01-01 03:00:00+01:00	2005	1	1	4	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.2	0.8	68	98414	...	99999	9	999999999	10	0.118	18	0	0.689	0.0	99.0
2005-01-01 04:00:00+01:00	2005	1	1	5	60	*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?*?	6.0	0.5	68	98414	...	99999	9	999999999	10	0.118	18	0	0.676	0.0	99.0

5 rows × 35 columns

import pandas as pd

summary = pd.read_csv('../data/processed/summary.csv')
summary.head()

	Zonenr	Rel_BAL	BAL_ENERGY	DQAIRdt	QHEAT	QCOOL	QINF	QVENT	QCOUPL	QTRANS	QGAININT	QWGAIN	QSOLGAIN	QSOLAIR
0	1	0.0	1.548000e-08	234.9	7300.0	0.0	0.0	0.0	-436900.0	429900.0	0.0	0.0	0.0	0.0
1	2	0.0	6.835000e-09	113.0	29990000.0	0.0	-38920000.0	0.0	8200000.0	-205300.0	0.0	0.0	939900.0	0.0
2	3	0.0	-7.202000e-09	1212.0	58960.0	0.0	-1469000.0	0.0	-813700.0	-5457000.0	1215000.0	0.0	6467000.0	0.0
3	4	0.0	-4.229000e-08	2167.0	20150.0	0.0	-1685000.0	0.0	-5523000.0	-9769000.0	4704000.0	0.0	12250000.0	0.0
4	5	0.0	6.735000e-09	1822.0	47730.0	0.0	-1008000.0	0.0	-2289000.0	-4017000.0	2227000.0	0.0	5041000.0	0.0

energy_zones = pd.read_csv('../data/processed/energy_zones.csv')
energy_zones.head()

	TIME	REL_BAL_ENERGY	1_B4_QBAL	1_B4_DQAIRdT	1_B4_QHEAT	1_B4_QCOOL	1_B4_QINF	1_B4_QVENT	1B4_QCOUP	1_B4_QTRANS	...	5_B4_QHEAT	5_B4_QCOOL	5_B4_QINF	5_B4_QVENT	5B4_QCOUP	5_B4_QTRANS	5_B4_QGINT	5_B4_QWGAIN	5_B4_QSOL	5_B4_QSOLAIR
0	0	0.0	2.523000e-11	-8.356	0.0	0.0	0.0	0.0	-41.42	33.07	...	0.0	0.0	-83.14	0.0	62.85	-554.3	374.9	0.0	-1.070000e-12	0.0
1	1	0.0	1.478000e-11	-2.190	0.0	0.0	0.0	0.0	-42.78	40.59	...	0.0	0.0	-56.99	0.0	97.38	-387.7	374.9	0.0	-2.884000e-13	0.0
2	2	0.0	3.435000e-12	2.555	0.0	0.0	0.0	0.0	-27.60	30.15	...	0.0	0.0	-36.10	0.0	82.31	-395.9	374.9	0.0	3.505000e-12	0.0
3	3	0.0	1.552000e-11	-4.190	0.0	0.0	0.0	0.0	-29.01	24.82	...	0.0	0.0	-37.67	0.0	72.04	-414.2	374.9	0.0	-1.149000e-12	0.0
4	4	0.0	5.542000e-13	-1.234	0.0	0.0	0.0	0.0	-27.87	26.64	...	0.0	0.0	-38.77	0.0	64.68	-407.8	374.9	0.0	-2.478000e-12	0.0

5 rows × 62 columns

cultural_e = pd.read_csv('../data/processed/cultural-e.csv')
cultural_e.head()

	TIME	SQHEAT_1	SQCOOL_1	TAIR_corridor	TOP_corridor	TAIR_bathroom	TOP_bathroom	TAIR_bed1_single	TOP_bed1_single	TAIR_livingRoom	...	label.53	label.54	label.55	label.56	label.57	label.58	label.59	label.60	label.61	label.62
0	0	9361.516515	0.0	21.885728	21.912881	21.0	20.771892	21.931716	21.859126	22.939062	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	1	9438.759632	0.0	21.808748	21.856483	21.0	20.766469	21.934159	21.823100	22.640732	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	2	6743.589743	0.0	21.750157	21.808868	21.0	20.769698	21.766984	21.725055	22.612469	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	3	4457.281870	0.0	21.752182	21.795807	21.0	20.773711	21.789886	21.736173	22.605993	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	4	4636.604799	0.0	21.743100	21.779023	21.0	20.770586	21.776703	21.706037	22.575662	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 101 columns

Graphs

from src.visualization import visualize as viz

Climate

viz.air_temperature(weather)

viz.relative_humidity(weather)

viz.horizontal_irradiance(weather)

Energy

viz.heating_loads(cultural_e)

viz.cooling_loads(cultural_e)

viz.energy_balance(summary)

for zone in ['1', '2', '3', '4', '5']:
    viz.zone_energy_balance(energy_zones, zone)

viz.monthly_consumption(cultural_e)

Comfort

viz.psychrochart(cultural_e.sample(n=100), 'bed1_single').get_figure()

viz.airt_heatmap(cultural_e, 'bed1_single')

viz.shd_heatmap(cultural_e)

viz.win_heatmap(cultural_e)

viz.iaq_co2(cultural_e, ['LR'], ['BR1', 'BR2'])

viz.relh(cultural_e, ['RELHUM_livingRoom', 'RELHUM_bed1_single','RELHUM_bed2_double'], ['SCH_PER_LR', 'SCH_PER_BR1', 'SCH_PER_BR2'])